SOTAVerified

Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 655 papers

TitleStatusHype
An Analysis of Ensemble Sampling—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Bandits Under The Influence (Extended Version)—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Bandit Learning for Diversified Interactive Recommendation—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Bag of Policies for Distributional Deep Exploration—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Automatic Ensemble Learning for Online Influence Maximization—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Adaptive Data Augmentation for Thompson Sampling—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Diffusion Approximations for Thompson Sampling—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
Aligning AI Agents via Information-Directed Sampling—0
Differentially Private Federated Bayesian Optimization with Distributed Exploration—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
Deep Hierarchy in Bandits—0
Deep Contextual Multi-armed Bandits—0
Asynchronous Multi Agent Active Search—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
Adaptive Combinatorial Allocation—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
A Batched Multi-Armed Bandit Approach to News Headline Testing—0
Deep Active Ensemble Sampling For Image Classification—0
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine—0
Deciding What to Learn: A Rate-Distortion Approach—0
Deep Exploration for Recommendation Systems—0
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
Debiasing Samples from Online Learning Using Bootstrap—0
Asymptotic Convergence of Thompson Sampling—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Show:102550
← PrevPage 4 of 14Next →

No leaderboard results yet.